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V1.6.1

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Bug fixes and split Dual Clip Loader into two parts
This commit is contained in:
WarpedAnimation
2025-05-27 21:53:16 -05:00
committed by GitHub
parent 954575608b
commit edbc323722
1 changed files with 86 additions and 40 deletions
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nodes.py
+86 -40
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@@ -56,7 +56,7 @@ from comfy.ldm.lightricks.symmetric_patchifier import latent_to_pixel_coords
from comfy.ldm.common_dit import rms_norm
from comfy.ldm.wan.model import sinusoidal_embedding_1d
from .diffusers_helper.memory import move_model_to_device_with_memory_preservation
from .diffusers_helper.memory import move_model_to_device_with_memory_preservation, offload_model_from_device_for_memory_preservation
from .diffusers_helper.k_diffusion_hunyuan import sample_hunyuan, sample_hunyuan2
from .diffusers_helper.utils import crop_or_pad_yield_mask, soft_append_bcthw
import math
@@ -5793,7 +5793,7 @@ class WarpedFramepackSampler:
self.positive = positive
self.negative = negative
self.device = mm.get_torch_device()
self.offload_device = mm.unet_offload_device()
self.offload_device = get_offload_device()
self.clip_vision = clip_vision_model
self.blend_frames = blend_frames
self.shift = shift
@@ -5803,6 +5803,8 @@ class WarpedFramepackSampler:
self.v2v_context_count = v2v_context_count
self.verbose_messaging = verbose_messaging
print("Device: {} | Offload Device: {}".format(self.device, self.offload_device))
if not video_image_batch is None:
if len(video_image_batch.shape) < 4:
video_image_batch = video_image_batch.unsqueeze(0)
@@ -6044,7 +6046,9 @@ class WarpedFramepackSampler:
latent_indices, clean_latents, clean_latent_indices, clean_latents_2x, clean_latent_2x_indices, clean_latents_4x, clean_latent_4x_indices, callback)
noise[latent_padding] = None
print("generated_latents {} | Shape: {}".format(latent_padding, generated_latents.shape))
if self.verbose_messaging:
print("generated_latents {} | Shape: {}".format(latent_padding, generated_latents.shape))
if not is_last_section:
history_latents = torch.cat([original_history_latents, generated_latents.to(original_history_latents)], dim=2)
@@ -6060,10 +6064,10 @@ class WarpedFramepackSampler:
break
except mm.InterruptProcessingException as ie:
interrupted = True
print(f"\nWarpedSamplerCustomAdv: Processing Interrupted.")
print("WarpedSamplerCustomAdv: Returning only partial results (if any).\n If zero images generated, a blank yellow image will be returned.\n")
print(f"\nWarpedFramepackSampler: Processing Interrupted.")
print("WarpedFramepackSampler: Returning only partial results (if any).\n If zero images generated, a blank yellow image will be returned.\n")
generation_status = f"\nWarpedSamplerCustomAdv: Processing Interrupted."
generation_status = f"\nWarpedFramepackSampler: Processing Interrupted."
pass
except BaseException as e:
@@ -6072,10 +6076,10 @@ class WarpedFramepackSampler:
gc.collect()
time.sleep(1)
print(f"\nWarpedSamplerCustomAdv: Exception During Processing: {str(e)}")
print("WarpedSamplerCustomAdv: Returning only partial results (if any).\n If zero images generated, a blank red image will be returned.\n")
generation_status = f"WarpedSamplerCustomAdv: Exception During Processing: {str(e)}"
generation_status = "{}{}".format(generation_status, "WarpedSamplerCustomAdv: Returning only partial results (if any).\nIf zero images generated, a blank red image will be returned.")
print(f"\nWarpedFramepackSampler: Exception During Processing: {str(e)}")
print("WarpedFramepackSampler: Returning only partial results (if any).\n If zero images generated, a blank red image will be returned.\n")
generation_status = f"WarpedFramepackSampler: Exception During Processing: {str(e)}"
generation_status = "{}{}".format(generation_status, "WarpedFramepackSampler: Returning only partial results (if any).\nIf zero images generated, a blank red image will be returned.")
traceback.print_tb(e.__traceback__, limit=99, file=sys.stdout)
@@ -6103,7 +6107,10 @@ class WarpedFramepackSampler:
video_latent_batches = None
video_image_batch = None
self.transformer.to(self.offload_device)
# self.transformer.to(self.offload_device)
offload_model_from_device_for_memory_preservation(self.transformer, self.device, preserved_memory_gb=0)
mm.unload_all_models()
mm.soft_empty_cache()
gc.collect()
@@ -6294,9 +6301,11 @@ class WarpedFramepackSampler:
latent_indices, clean_latents, clean_latent_indices, clean_latents_2x, clean_latent_2x_indices, clean_latents_4x, clean_latent_4x_indices, callback)
noise[latent_padding] = None
print("generated_latents for batch {}: Shape: {}\n".format(latent_padding, generated_latents.shape))
latent_batches_gend.append(generated_latents.clone().detach() / vae_scaling_factor)
if self.verbose_messaging:
print("generated_latents for batch {}: Shape: {}\n".format(latent_padding, generated_latents.shape))
latent_batches_gend.append(generated_latents.clone().detach() / vae_scaling_factor)
total_generated_latent_frames += int(generated_latents.shape[2])
generated_latents.to(dtype=torch.float32, device=self.offload_device)
@@ -6329,10 +6338,10 @@ class WarpedFramepackSampler:
break
except mm.InterruptProcessingException as ie:
interrupted = True
print(f"\nWarpedSamplerCustomAdv: Processing Interrupted.")
print("WarpedSamplerCustomAdv: Returning only partial results (if any).\n If zero images generated, a blank yellow image will be returned.\n")
print(f"\nWarpedFramepackSampler: Processing Interrupted.")
print("WarpedFramepackSampler: Returning only partial results (if any).\n If zero images generated, a blank yellow image will be returned.\n")
generation_status = f"\nWarpedSamplerCustomAdv: Processing Interrupted."
generation_status = f"\nWarpedFramepackSampler: Processing Interrupted."
pass
except BaseException as e:
@@ -6341,10 +6350,10 @@ class WarpedFramepackSampler:
gc.collect()
time.sleep(1)
print(f"\nWarpedSamplerCustomAdv: Exception During Processing: {str(e)}")
print("WarpedSamplerCustomAdv: Returning only partial results (if any).\n If zero images generated, a blank red image will be returned.\n")
generation_status = f"WarpedSamplerCustomAdv: Exception During Processing: {str(e)}"
generation_status = "{}{}".format(generation_status, "WarpedSamplerCustomAdv: Returning only partial results (if any).\nIf zero images generated, a blank red image will be returned.")
print(f"\nWarpedFramepackSampler: Exception During Processing: {str(e)}")
print("WarpedFramepackSampler: Returning only partial results (if any).\n If zero images generated, a blank red image will be returned.\n")
generation_status = f"WarpedFramepackSampler: Exception During Processing: {str(e)}"
generation_status = "{}{}".format(generation_status, "WarpedFramepackSampler: Returning only partial results (if any).\nIf zero images generated, a blank red image will be returned.")
traceback.print_tb(e.__traceback__, limit=99, file=sys.stdout)
@@ -6373,7 +6382,9 @@ class WarpedFramepackSampler:
video_image_batch = None
latent_embeds = None
self.transformer.to(self.offload_device)
# self.transformer.to(self.offload_device)
offload_model_from_device_for_memory_preservation(self.transformer, self.device, preserved_memory_gb=0)
mm.unload_all_models()
mm.soft_empty_cache()
gc.collect()
@@ -6999,35 +7010,78 @@ def get_available_devices():
available_devices = ["cpu"]
if torch.cuda.is_available():
if torch.cuda.device_count() < 2:
available_devices.append("cuda")
else:
available_devices.append("cuda")
if torch.cuda.device_count() > 1:
for i in range(torch.cuda.device_count()):
temp_device = "cuda:{}".format(i)
available_devices.append(temp_device)
return available_devices
class WarpedDualCLIPLoaderAdvanced(ComfyNodeABC):
class WarpedDualEncoder(ComfyNodeABC):
@classmethod
def INPUT_TYPES(self):
return {"required": { "clip": ("CLIP", ),
"positive_text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The positive prompt to be encoded."}),
"negative_text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The negative prompt to be encoded."}),
},
}
RETURN_TYPES = (IO.CONDITIONING, IO.CONDITIONING, )
RETURN_NAMES = ("pos_conditioning", "neg_conditioning", )
FUNCTION = "process"
CATEGORY = "Warped/Conditioning"
DESCRIPTION = "Encodes both positive and negative prompts."
def process(self, clip, positive_text="", negative_text=""):
if clip.patcher.model.device != clip.patcher.load_device:
clip.patcher.model.to(device=clip.patcher.load_device)
print("WarpedDualEncoder: Encoding Prompts...")
positive_conditioning = self.encode(clip, positive_text)
negative_conditioning = self.encode(clip, negative_text)
print("WarpedDualEncoder: Encoding Prompts...Done.")
clip.patcher.model.to(device=get_offload_device())
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
time.sleep(1)
return (positive_conditioning, negative_conditioning, )
def encode(self, clip, text):
if clip is None:
raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
tokens = clip.tokenize(text)
return_encoding = clip.encode_from_tokens_scheduled(tokens)
return return_encoding
class WarpedDualCLIPLoader:
@classmethod
def INPUT_TYPES(self):
return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
"clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
"type": (["sdxl", "sd3", "flux", "hunyuan_video"], ),
"device": (get_available_devices(), {"default": "cpu"}),
"positive_text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The positive prompt to be encoded."}),
"negative_text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The negative prompt to be encoded."}),
},
}
RETURN_TYPES = ("CLIP", IO.CONDITIONING, IO.CONDITIONING, )
RETURN_NAMES = ("clip", "pos_conditioning", "neg_conditioning", )
RETURN_TYPES = ("CLIP", )
RETURN_NAMES = ("clip", )
FUNCTION = "load_clip"
CATEGORY = "Warped/Loaders"
DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5"
def load_clip(self, clip_name1, clip_name2, type, device="default", positive_text="", negative_text=""):
def load_clip(self, clip_name1, clip_name2, type, device="default"):
clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
if type == "sdxl":
@@ -7057,15 +7111,7 @@ class WarpedDualCLIPLoaderAdvanced(ComfyNodeABC):
clip = self.sd_load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
if clip.patcher.model.device != model_options["load_device"]:
clip.patcher.model.to(device=model_options["load_device"])
positive_conditioning = self.encode(clip, positive_text)
negative_conditioning = self.encode(clip, negative_text)
clip.patcher.model.to(device=model_options["offload_device"])
return (clip, positive_conditioning, negative_conditioning, )
return (clip, )
def sd_load_clip(self, ckpt_paths, embedding_directory=None, clip_type=comfy.sd.CLIPType.STABLE_DIFFUSION, model_options={"offload_device": get_offload_device()}):
checkpoint_temp = []
@@ -7112,7 +7158,7 @@ class WarpedLoadFramePackModel:
"base_precision": (["fp32", "bf16", "fp16"], {"default": "bf16"}),
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2'], {"default": 'disabled', "tooltip": "optional quantization method"}),
"load_device": (["main_device", "offload_device"], {"default": "cuda", "tooltip": "Initialize the model on the main device or offload device"}),
"load_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Initialize the model on the main device or offload device"}),
},
"optional": {
"attention_mode": ([